Innovative shipment methods have emerged in response to technological innovation, increasing performance demand and a changing retail landscape. Among them, crowdshipping is built on the idea that citizens can connect via online platforms and deliver goods to each other along planned travel routes. While crowd-logistics companies highlight the potentials for saving money, optimizing delivery operations, creating social connections, and reducing the energy footprint, uptake is still limited. The goal of our research is to study the behavioral factors that influence new on-demand systems like crowdshipping. We are studying motivations and acceptance of both the citizen-drivers and the system users. The models we are developing from real operator data as well as from hypothetical experiments is the first of it's kind. It will help the industry and research community understand crowd-inspired logistics with delivery by occasional drivers. Our work has given evidence of the trade-offs by crowd-carriers and the heterogeneity in the sender market. This work will have several benefits, a) understand consumer preferences and motivations and improve their experience, b) forecast behavior of drivers and other stake-holders in the context of new logistics initiatives, c) improve company practices and policy incentives for recruitment, d) design sustainable business strategies, e) minimize undesired effects such as increased mileage for deliveries. Targeted research will help to build the “critical mass” necessary to establish a sustainable human-centered delivery system that ensures societal benefits.

Individual and Community acceptance of mobility innovation

Emerging mobility solutions are likely to, over time, alter models of vehicle ownership and patterns of land use, generate new markets and economic opportunities and affect road vehicle energy consumption and greenhouse gas (GHG).

We are developing research on acceptance and adaptation in the short and long-term related to emerging mobility systems. Little is known about the effect on either short-term mobility decisions (travel patterns and mode choice) and long-term choices (such as car ownership). Given the recent emergence of shared or Mobility-as-a-service systems, and lack of market data, there are significant challenges in understanding and forecasting demand and long-term mobility impacts. The research seeks to answer the following:

What are the motivations to participate in now mobility systems?

How does the the built environment, including neighborhood characteristics, the community values, and transportation system quality, affect well-being (individual and social) of citizens by transportation choices (e.g., using active modes or mobility sharing instead of a car)?

What changes to mobility patterns are expected (short/long-run)?

The research on acceptance of new collaborative consumption systems provide important insights for station location, service design and selection of the right incentives to favor uptake and efficient usage.

Research will enable smarter designs of emerging systems that maximize acceptance and triggers the positive potential linked to decreasing emissions and promoting multi-modality.

Modeling decisions that do not follow model assumptions

The traditional view of decision-making that is represented in transportation models assumes that choices can be represented by linear compensatory models. A choice strategy can be described as compensatory if there is trading among attributes, that is, disadvantages in one choice characteristic can be traded against (offset by) advantages in another. The notion that people are willing and able to carry out these trade-offs is fundamental to the use and interpretation of choice data. Behavioural theorists have long sustained that decision-makers are not necessarily fully informed, consistent or utility maximising when making choices. My research has explored several alternative decision-making frameworks, such as reference-dependence, along with model frameworks that allow different decision rules to co-exist in a population. Later work has additionally begun to examine the factors that cause respondents to rely on different decision strategies. The consistent evidence that decision-making deviates from standard assumptions has mobilized the research community to propose better models. It is still unclear what the effective consequences are for policy design and practical use of model for planning.

Rethinking theories of adoption for transformative mobility

Transportation analysts are constantly faced with the challenge of understanding transformative new services, modes or mobility solutions, typically at early stages of conception and in the absence of market data. The goal of this research project is to explore methods to theorize, model and collect data to help our understanding of adoption paths for transportation innovations.

The current practice in transportation demand modeling relies on static representations of travel behavior mainly based on discrete choice models with only a single point of data collection. There is growing interest in among mobility researchers in new approaches that view choices as a complex process. From continuum models such as the Theory of Planned Behavior where behavior is guided by intentions to stage models such as the Transtheoretical Model where behavior is seen as a process of discrete stages to reach given goals. Ongoing research in the lab is developing new models that incorporate change constructs to improve prediction of who will opt in to innovative systems, such as bike-sharing schemes, as well as well as the continuance and changes in use that are essential for innovations to succeed.

Electrifying transportation is a complex process that involves numerous infrastructure planning decisions (e.g. charging networks and electrical power grid), vehicle design and emission standard regulation and tax incentive policies. The effectiveness of these policies depends critically on how soon and how many conventional car users transition to electric vehicles (EV), especially plug-in EVs (PEV). Despite many efforts that aim at promoting EVs, the latest US data on PEV represent less than 1% of new car sales.

This project aims to create an joint behavior-optimization framework that anticipates human-policy interactions to support decision making, and to develop evaluation metrics and methodologies to prioritize policies of electrification of vehicle fleets.

The behavior research aims to understand and predict consumer vehicle choice and use behaviors under the impact of various policies, by developing new econometric models based on empirical data. The investigation focuses on understanding the transaction dynamics and the threshold concepts with strong ties to optimization models, in an inherently uncertain market environment.

Stats & Figures

Some key findings of general interest

1 in 5

trips involve trip-chaining in which people sandwich in daily errands and activities while on the way to and from work [USDOT FHWA].

70 %

of person-miles of travel (PMT) in the United States is done in cars or other personal vehicles [USDOC CENSUS 2016]

Biking and walking

Only a small percentage of people walk or bike to work in US. Nonmotorized modes of commuting are important in cities. According to the 2008–2012 American Community Survey in the 50 largest U.S. cities, 5.0 percent of workers walked to work and another 1.0 percent biked. [ACS 2015]